Gaussian process latent class choice models

•Integration of machine learning and discrete choice models.•New choice model referred to as Gaussian process latent class choice model.•Derivation and implementation of an expectation-maximization algorithm.•More complex and flexible representation of unobserved heterogeneity.•The model improves pr...

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Bibliographic Details
Published in:Transportation research. Part C, Emerging technologies Vol. 136; p. 103552
Main Authors: Sfeir, Georges, Rodrigues, Filipe, Abou-Zeid, Maya
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.03.2022
Subjects:
Discrete choice models
EM algorithm
Gaussian process
Latent class choice models
Machine learning
Latent class choice models
Gaussian process
Discrete choice models
EM algorithm
Machine learning
ISSN:0968-090X, 1879-2359
Online Access:Get full text
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Summary:•Integration of machine learning and discrete choice models.•New choice model referred to as Gaussian process latent class choice model.•Derivation and implementation of an expectation-maximization algorithm.•More complex and flexible representation of unobserved heterogeneity.•The model improves prediction accuracy without weakening economic interpretability. We present a Gaussian Process – Latent Class Choice Model (GP-LCCM) to integrate a non-parametric class of probabilistic machine learning within discrete choice models (DCMs). Gaussian Processes (GPs) are kernel-based algorithms that incorporate expert knowledge by assuming priors over latent functions rather than priors over parameters, which makes them more flexible in addressing nonlinear problems. By integrating a Gaussian Process within a LCCM structure, we aim at improving discrete representations of unobserved heterogeneity. The proposed model would assign individuals probabilistically to behaviorally homogeneous clusters (latent classes) using GPs and simultaneously estimate class-specific choice models by relying on random utility models. Furthermore, we derive and implement an Expectation-Maximization (EM) algorithm to jointly estimate/infer the hyperparameters of the GP kernel function and the class-specific choice parameters by relying on a Laplace approximation and gradient-based numerical optimization methods, respectively. The model is tested on two different mode choice applications and compared against different LCCM benchmarks. Results show that GP-LCCM allows for a more complex and flexible representation of heterogeneity and improves both in-sample fit and out-of-sample predictive power. Moreover, behavioral and economic interpretability is maintained at the class-specific choice model level while local interpretation of the latent classes can still be achieved, although the non-parametric characteristic of GPs lessens the transparency of the model.
ISSN:0968-090X
1879-2359
DOI:10.1016/j.trc.2022.103552